基于树的差分测试使用RNA-seq的推断不确定性
Noor P Singh1, Euphy Wu2, Jason Fan1
1University of Maryland.
Genome research
|August 21, 2025
概括
鉴定差异性基因表达是由于转录丰度的不确定性而具有挑战性. 我们的新方法mehenDi使用树结构来发现显著的表达变化,
科学领域:
- 基因组学
- 生物信息学
- 计算生物学
背景情况:
- 对RNA测序数据的差异表达分析对于生物见解至关重要.
- 转录丰度估计的不确定性可能导致假阳性或减少统计能力.
- 现有的方法往往难以有效地将这种不确定性纳入差异测试中.
研究的目的:
- 介绍mehenDi,一种用于差异转录表达分析的新方法.
- 利用层次树结构 (TreeTerminus) 来管理转录丰度的不确定性.
- 识别差异表达的转录和转录组,包括树的内部节点.
主要方法:
- 使用TreeTerminus等级结构来表示转录关系和不确定性.
- 开发了基于数据的树结构差异测试方法.
- 选择节点 (转录或内部节点) 来最大化信号,同时控制丰度不确定性.
- 将mehenDi与现有的基于树的和不确定性的差异表达方法进行比较.
主要成果:
- mehenDi成功识别了差异表达的内部节点,揭示了仅通过转录分析错过的信号.
- 该方法在模拟和实验RNA-seq数据集上表现出强大的性能.
- mehenDi有效地平衡了差异表达的检测和不确定性的控制.
结论:
- mehenDi通过结合转录层次结构和不确定性提供了差异表达分析的先进方法.
- 该方法增强了生物相关差异表达信号的发现.
- mehenDi提供了一个强大的转录数据解释工具.
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